Learning sparse models for image quality assessment

Many successful image quality metrics rely on the structural information in an image to assess its perceptual quality. Extracting the structural information that is perceptually meaningful to our visual system, however, is a challenging task. This paper proposes a new quality assessment metric that relies on a sparse modeling approach to learn the inherent structures of the image. These structures are learnt as a set of basis vectors, such that any structure in the image can be represented by a linear combination of only a few of these basis vectors. This strategy is known to generate basis vectors that are qualitatively similar to the receptive field of the simple cells present in the mammalian primary visual cortex. The perceptual quality of the distorted image is estimated by comparing the structures of the reference and the distorted images in terms of the learnt basis vectors. Our approach is evaluated on five standard subject-rated image quality assessment datasets. The proposed metric exhibits high correlation with the subjective ratings outperforming several well established methods.